寻找生命：从合成数据中学习以检测视频中的生命体征
In Search of Life: Learning from Synthetic Data to Detect Vital Signs in Videos
摘要：
在计算机视觉中，自动检测视频中的生命体征，例如心率和呼吸率的估计，是具有挑战性的研究问题，在医学领域具有重要应用。解决这一任务的关键困难之一是缺乏足够的监督训练数据，这严重限制了强大的深度神经网络的使用。在本文中，我们通过一种新的深度学习方法来解决这一限制，在这种方法中，训练一个循环的深度神经网络，从纯粹的合成数据中检测红外热域中的生命体征。最令人惊讶的是，我们用于合成训练数据生成的新方法是通用的，相对简单的，并且几乎不使用先前的医学领域知识。此外，我们的系统以纯自动的方式进行训练，不需要人工注释，还可以学习预测每个时刻的呼吸或心脏强度信号，并检测与给定任务最相关的感兴趣区域，例如呼吸情况下的鼻子区域。我们在最近的LCAS数据集上测试了我们提出的系统的有效性，并获得了最先进的结果。
英文摘要：
Automatically detecting vital signs in videos, such as the estimation of heart and respiration rates, is a challenging research problem in computer vision with important applications in the medical field. One of the key difficulties in tackling this task is the lack of sufficient supervised training data, which severely limits the use of powerful deep neural networks. In this paper we address this limitation through a novel deep learning approach, in which a recurrent deep neural network is trained to detect vital signs in the infrared thermal domain from purely synthetic data. What is most surprising is that our novel method for synthetic training data generation is general, relatively simple and uses almost no prior medical domain knowledge. Moreover, our system, which is trained in a purely automatic manner and needs no human annotation, also learns to predict the respiration or heart intensity signal for each moment in time and to detect the region of interest that is most relevant for the given task, e.g. the nose area in the case of respiration. We test the effectiveness of our proposed system on the recent LCAS dataset and obtain state-of-the-art results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
